CN110517246A - A kind of image processing method, device, electronic equipment and storage medium - Google Patents
A kind of image processing method, device, electronic equipment and storage medium Download PDFInfo
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Abstract
The present invention provides a kind of image processing method, device, electronic equipment and storage mediums, are related to image processing techniques, and method includes: to extract the characteristic information of image, are classified according to the characteristic information to described image；When being implanted with interference content in classification results characterization described image, the boundary position between the interference content and original contents in described image is detected；According to the distribution mode for the interference content that the boundary position detected and the classification results characterize, the visual range region that the original contents are presented in described image is determined；Determine the area accounting in the visual range region；When the area accounting in the visual range region is less than area accounting threshold value, determine that described image belongs to the abnormal image for influencing viewing experience.By means of the invention it is possible to the visual range region of the original contents of image be identified, to judge whether image belongs to the abnormal image for influencing viewing experience.
Description
Technical field
The present invention relates to the computer vision technique of artificial intelligence more particularly to a kind of image processing method, device, electronics
Equipment and storage medium.
Background technique
Artificial intelligence (AI, Artificial Intelligence) is to utilize digital computer or digital computer control
Machine simulation, extension and the intelligence for extending people of system, perception environment obtain knowledge and the reason using Knowledge Acquirement optimum
By, methods and techniques and application system.
Computer vision technique (CV, Computer Vision) is the important application of artificial intelligence, studies relevant reason
By and technology, it is intended to the artificial intelligence system of information can be obtained from image or multidimensional data by establishing.Typical computer
Vision technique generally includes image procossing.
Increasingly developed with Internet technology, the media data of the image formats such as video and photo has become greatly
The main body of data suppresses low-quality image to improving user experience extremely including some second-rate videos and photo
Close important, certain from media is to reduce cost of manufacture to carry outer station image, can be right in order to hide duplicate checking mechanism in handling process
Image carries out secondary editor, such as is inserted into black region, frosted glass region in image two sides, or unrelated with picture original contents
Advertising information or other interference patterns, such photos and videos will affect the viewing experience of user.
Summary of the invention
The embodiment of the present invention provides a kind of image processing method, device, electronic equipment and storage medium, can identify judgement
Whether image belongs to the abnormal image for influencing viewing experience.
The technical solution of the embodiment of the present invention is achieved in that
The embodiment of the present invention provides a kind of image processing method, comprising:
The characteristic information for extracting image, classifies to described image according to the characteristic information；
When being implanted with interference content in classification results characterization described image, the interference content and original in described image are detected
Boundary position between beginning content；
According to the distribution side for the interference content that the boundary position detected and the classification results characterize
Formula determines the visual range region that the original contents are presented in described image；
Determine the area accounting in the visual range region；
When the area accounting in the visual range region is less than area accounting threshold value, determine that described image belongs to influence and sees
See the abnormal image of experience.
The embodiment of the present invention provides a kind of image processing apparatus, comprising:
Image classification module divides described image according to the characteristic information for extracting the characteristic information of image
Class；
Boundary detection module, for detecting the figure when being implanted with interference content in classification results characterization described image
The boundary position between interference content and original contents as in；
Visual range region confirmation module, boundary position and the classification results table for being detected according to
The distribution mode of the interference content of sign, determines the visual range region that the original contents are presented in described image；
Area accounting determining module, for determining the area accounting in the visual range region；
Image judgment module, for determining when the area accounting in the visual range region is less than area accounting threshold value
Described image belongs to the abnormal image for influencing viewing experience.
In above scheme, described image categorization module is also used to:
Feature is extracted from described image by Classification Neural model, is to respectively correspond by extracted Feature Conversion
The probability of following classification results:
The potential abnormal image for not being implanted with the normal picture of interference content, being implanted with interference content；
Wherein, the potential abnormal image is implanted with interference with any one presentation mode in a variety of presentation modes of priori
Content.
In above scheme, the boundary detection module is also used to:
Described image is pre-processed to remove the noise in described image；
Gradient magnitude and the direction of each pixel in described image are determined, to obtain the candidate of edge pixel point
Collection；
The Candidate Set of the edge pixel point is carried out simplifying processing, the Candidate Set shape based on the edge pixel point after simplifying
At the profile diagram of described image；
Based on the profile diagram, the boundary position between the interference content and original contents in described image is obtained.
In above scheme, the boundary detection module is also used to:
Retain the pixel that gradient intensity in the profile diagram is greater than first gradient threshold value, and inhibits gradient intensity less than the
The pixel of two Grads threshold, wherein the first gradient threshold value is greater than second Grads threshold；
By pixel of the gradient intensity between the first gradient threshold value and second Grads threshold, it is labeled as weak side
Edge pixel；
When the pixel for thering is gradient intensity to be greater than the first gradient threshold value in the weak edge pixel neighborhood of a point, protect
Stay the weak edge pixel point, when in the weak edge pixel neighborhood of a point without gradient intensity be greater than the first gradient threshold value
Pixel when, inhibit the weak edge pixel point；
Based on the pixel retained in the profile diagram combine the interference content to be formed in described image and original contents it
Between boundary position.
In above scheme, the boundary detection module is also used to:
Determine white pixel point proportion threshold value；
The profile diagram is traversed, the ratio of the white pixel point of every a line in the profile diagram is obtained；
The position of the row of the white pixel point proportion threshold value will be greater than in the profile diagram, be determined as in described image
Interfere the boundary position between content and original contents.
In above scheme, the boundary detection module is also used to:
White pixel point proportion threshold value is determined according to the sharpness of border degree of the interference content, wherein the white pixel
Point proportion threshold value and the sharpness of border degree of the interference content are positively correlated.
In above scheme, the boundary detection module is also used to:
When the interference content is monochrome image, determine that the white pixel point proportion threshold value is the first proportion threshold value；
When the interference content is frosted glass image, determine that the white pixel point proportion threshold value is the second ratio threshold
Value；
When the interference content is other images other than the monochrome image and the frosted glass image, determine
The white pixel point proportion threshold value is third proportion threshold value；
Wherein, the third proportion threshold value is greater than second proportion threshold value, and the third proportion threshold value is less than described
First proportion threshold value.
In above scheme, described device further include:
Sliding average variance detection module, is used for:
When the abnormal image includes multiple same or similar subgraphs, carried out at sliding window in each subgraph
Reason, and detect the standard deviation of the grayscale image pixel value of same position window in each subgraph；
When the difference between the standard deviation is less than standard deviation threshold method, determine that the image of the same position window is phase
It is determined as similar window like image, and by the same position window；
When the accounting of the quantity of the similar window is greater than similar window accounting threshold value, by the classification of the abnormal image
As a result it is updated to normal picture.
In above scheme, described device further include: Video decoding module and video judgment module,
The Video decoding module obtains multiframe described image for decoding from video；
The video judgment module, for when the number for belonging to the abnormal image in the multiframe described image that decoding obtains
When greater than outlier threshold, determine that the video belongs to the anomalous video for influencing viewing experience.
The embodiment of the present invention provides a kind of image processing electronics, comprising:
Memory, for storing executable instruction；
Processor when for executing the executable instruction stored in the memory, is realized provided in an embodiment of the present invention
Image processing method.
The embodiment of the present invention provides a kind of storage medium, is stored with executable instruction, real when for causing processor to execute
Existing image processing method provided in an embodiment of the present invention.
The embodiment of the present invention has the advantages that
By the type for being classified to obtain image to image, the type based on image carries out edge detection and positions boundary
Line, so as to identify image original contents visual range, and judge image whether belong to influence viewing experience it is different
Normal image.
Detailed description of the invention
Fig. 1 is that the optional structure of image processing system framework provided in an embodiment of the present invention in practical applications is shown
It is intended to；
Fig. 2 is that the embodiment of the present invention provides an optional structural schematic diagram of image processing apparatus；
Fig. 3 A-3E is the optional flow diagram of image processing method provided in an embodiment of the present invention；
Fig. 4 is the direction schematic diagram of operator provided in an embodiment of the present invention；
Fig. 5 A is the image schematic diagram that interference content provided in an embodiment of the present invention is black region；
Fig. 5 B is the image schematic diagram that interference content provided in an embodiment of the present invention is frosted glass region；
Fig. 5 C is that interference content provided in an embodiment of the present invention is other other than monochrome image and frosted glass image
The image schematic diagram of image；
Fig. 6 A and Fig. 6 B are the adaptation application scenarios signals of sliding average variance detection technique provided in an embodiment of the present invention
Figure；
Fig. 7 is the principle flow chart of image processing method provided in an embodiment of the present invention；
Fig. 8 is image processing method provided in an embodiment of the present invention for content auditing and the flow chart being recommended to use.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention make into
It is described in detail to one step, described embodiment is not construed as limitation of the present invention, and those of ordinary skill in the art are not having
All other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
In the following description, it is related to " some embodiments ", which depict the subsets of all possible embodiments, but can
To understand, " some embodiments " can be the same subsets or different subsets of all possible embodiments, and can not conflict
In the case where be combined with each other.
In the following description, related term " first second third " be only be the similar object of difference, no
Represent the particular sorted for being directed to object, it is possible to understand that ground, " first second third " can be interchanged specific in the case where permission
Sequence or precedence so that the embodiment of the present invention described herein can be other than illustrating herein or describing
Sequence is implemented.
Unless otherwise defined, all technical and scientific terms used herein and belong to technical field of the invention
The normally understood meaning of technical staff is identical.Term used herein is intended merely to the purpose of the description embodiment of the present invention,
It is not intended to limit the present invention.
Before the embodiment of the present invention is further elaborated, to noun involved in the embodiment of the present invention and term
It is illustrated, noun involved in the embodiment of the present invention and term are suitable for following explanation.
1) original contents: actual content to be expressed in the images such as photo or video frame.
2) content: the advertising information added in the original contents of image perhaps black surround region or frosted glass area is interfered
The content of the influence such as domain user's viewing experience.
3) visual range region: the region of original contents for rendering in image.
Content is largely produced since the media at present, including some second-rate images including video
Resource, suppress low-quality image resource to improve user experience it is most important, it is certain from media be reduce cost of manufacture can carry
Outer station video can carry out secondary editor to hide duplicate checking mechanism to video, such as black region, hair glass are inserted into picture two sides
Glass region, or the advertising information unrelated with picture medium content and other interference contents.Such image resource meeting
Influence the viewing experience of user, it is therefore desirable to which machine automatic identification intercept or side is being recommended to suppress.
In the related art, mainly by detection image two sides, whether there is or not black surrounds, traverse image pixel square row wise or column wise
Battle array calculates the accounting of black pixel point to judge to have in image black region, and the applicable scene of this method is single, is not suitable for figure
Picture two sides are with complex scenes such as advertising sign, frosted glass special efficacy, static main bodys, and for this problem, the embodiment of the present invention is mentioned
A kind of image processing method out distinguishes picture type by disaggregated model, and disaggregated model can learn to different types of feature,
Strong with stronger generalization, the type for being based further on image carries out edge detection positioning boundary line, so as to identify
The visual range of the original contents of image, and judge whether image belongs to the abnormal image for influencing viewing experience, finally by cunning
Dynamic average variance detection technique avoids accidentally recalling, and improves recognition accuracy.
The embodiment of the present invention provides a kind of image processing method, device, electronic equipment and storage medium, can recognize that figure
The visual range region of the original contents of picture, and judge whether image belongs to the abnormal image for influencing viewing experience, illustrate below
The exemplary application of electronic equipment provided in an embodiment of the present invention, electronic equipment provided in an embodiment of the present invention may be embodied as pen
Remember this computer, tablet computer, desktop computer, set-top box, mobile device (for example, mobile phone, portable music player,
Personal digital assistant, specific messages equipment, portable gaming device) etc. various types of user terminals, also may be embodied as taking
Business device.In the following, will illustrate exemplary application when electronic equipment is embodied as server in conjunction with image processing system.
It is an optional configuration diagram of image processing system 100 provided in an embodiment of the present invention referring to Fig. 1, Fig. 1,
Both terminal 400 connects electronic equipment 200 by network 300, and network 300 can be wide area network or local area network, or be
Combination.
It include electronic equipment 200, user terminal 400 and document storage system 500, figure in described image processing system 100
As recommender system 600 and image data interception library 700.Electronic equipment 200 reads image from document storage system 500, passes through
Image processing method provided in an embodiment of the present invention determines whether read image is the abnormal image for influencing viewing experience, clothes
Device be engaged according to the judging result of image, the abnormal image that will affect viewing experience is intercepted or suppressed, and it is carried out
The image for intercepting and suppressing is sent to image data interception library 700, with the training and study for model, will not belong to influence to see
See that the image of the abnormal image of experience is pushed to recommender system, after recommender system is recalled and is sorted, by image recommendation
To user terminal.
Referring to fig. 2, Fig. 2 is the structural schematic diagram of the electronic equipment provided in an embodiment of the present invention for image procossing, Fig. 2
Shown in structural schematic diagram can be suitable for terminal and server, it is selective according to actual needs to implement therein group
Part.Electronic equipment 200 shown in Fig. 2 includes: at least one processor 21 0, memory 250, at least one network interface 220
With user interface 230.Various components in electronic equipment 200 are coupled by bus system 240.It is understood that total linear system
System 240 is for realizing the connection communication between these components.Bus system 240 further includes power supply in addition to including data/address bus
Bus, control bus and status signal bus in addition.But for the sake of clear explanation, various buses are all designated as bus in Fig. 2
System 240.
Processor 210 can be a kind of IC chip, the processing capacity with signal, such as general processor, number
Word signal processor (DSP, Digital Signal Processor) either other programmable logic device, discrete gate or
Transistor logic, discrete hardware components etc., wherein general processor can be microprocessor or any conventional processing
Device etc..
User interface 230 include make it possible to present one or more output devices 231 of media content, including one or
Multiple loudspeakers and/or one or more visual display screens.User interface 230 further includes one or more input units 232, packet
Include the user interface component for facilitating user's input, for example keyboard, mouse, microphone, touch screen display screen, camera, other are defeated
Enter button and control.
Memory 250 can be it is removable, it is non-removable or combinations thereof.Illustrative hardware device includes that solid-state is deposited
Reservoir, hard disk drive, CD drive etc..Memory 250 optionally includes one geographically far from processor 210
A or multiple storage equipment.
Memory 250 includes volatile memory or nonvolatile memory, may also comprise volatile and non-volatile and deposits
Both reservoirs.Nonvolatile memory can be read-only memory (ROM, Read Only Me mory), and volatile memory can
To be random access memory (RAM, Random Access Memo ry).The memory 250 of description of the embodiment of the present invention is intended to
Memory including any suitable type.
In some embodiments, memory 250 can storing data to support various operations, the example of these data includes
Program, module and data structure or its subset or superset, below exemplary illustration.
Operating system 251, including for handle various basic system services and execute hardware dependent tasks system program,
Such as ccf layer, core library layer, driving layer etc., for realizing various basic businesses and the hardware based task of processing；
Network communication module 252, for reaching other calculating via one or more (wired or wireless) network interfaces 420
Equipment, illustrative network interface 220 include: bluetooth, Wireless Fidelity (WiFi) and universal serial bus (USB,
Universal Serial Bus) etc.；
Module 253 is presented, for via one or more associated with user interface 230 output device 23 1 (for example,
Display screen, loudspeaker etc.) make it possible to present information (for example, for operating peripheral equipment and showing the user of content and information
Interface)；
Input processing module 254, for one to one or more from one of one or more input units 232 or
Multiple user's inputs or interaction detect and translate input or interaction detected.
In some embodiments, image processing apparatus provided in an embodiment of the present invention can be realized using software mode, Fig. 2
The image processing apparatus 255 being stored in memory 250 is shown, can be the software of the forms such as program and plug-in unit, including
Following software module: image classification module 2551, boundary detection module 255 2, visual range region confirmation module 2553, area
Accounting determining module 2554 and image judgment module 255 5, sliding average variance detection module 2556, Video decoding module 2557
It with video judgment module 2558, can be embedded in various clients, these modules are in logic, therefore according to being realized
Function can be combined arbitrarily or further split, and will hereinafter illustrate the function of modules.
In further embodiments, image processing apparatus provided in an embodiment of the present invention can be realized using hardware mode,
As an example, image processing apparatus provided in an embodiment of the present invention can be the processor using hardware decoding processor form,
It is programmed to perform image processing method provided in an embodiment of the present invention, for example, the processor of hardware decoding processor form
One or more application specific integrated circuit (ASIC, Application Specific Integrated can be used
Circuit), DSP, programmable logic device (PLD, Progr ammable Logic Device), complicated programmable logic device
Part (CPLD, Complex Programma ble Logic Device), field programmable gate array (FPGA, Field-
Programmable Gate Ar ray) or other electronic components.
Referring to Fig. 3 A, Fig. 3 A is an optional flow diagram of image processing method provided in an embodiment of the present invention,
The step of showing in conjunction with Fig. 3 A, is illustrated, the step of following methods can above-mentioned any type of electronic equipment (such as
Terminal or server) on realize.
In a step 101, the characteristic information for extracting image classifies to image according to characteristic information.
By taking electronic equipment is server as an example, server can receive the image of user's upload, and image here can be with source
In video file or photo files.Server extracts the characteristic information of image, is carried out according to the characteristic information of image to image
Classification.
Referring to Fig. 3 B, it is based on Fig. 3 A, the characteristic information of image is extracted in step 101, image is carried out according to characteristic information
Classification can be realized especially by step 1011.
In step 1011, feature is extracted from image by Classification Neural model, by extracted Feature Conversion
For the probability for respectively corresponding following classification results:
The potential abnormal image for not being implanted with the normal picture of interference content, being implanted with interference content；
Wherein, potential abnormal image is implanted in interference with any one presentation mode in a variety of presentation modes of priori
Hold.
As an example, Classification Neural model can be Inception V3 model, the lift scheme of the relevant technologies
Can mode be depth and width by increasing network, still, the shortcomings that this mode be due to network structure depth and
The increase of width, parameter amount, which can increase with it, results in the need for more computing resources, and is easy to appear over-fitting.Together
When, with the intensification of network, gradient is easy disperse, and model is not easy to restrain, and the selection of convolution kernel size also will affect model
Performance.Such issues that in order to solve, Incepti on V3 model obtain different scale using multiple convolution kernels of different sizes
Feature, and the level using the convolution kernel of 1*1 in channel carries out dimensionality reduction, reduces number of parameters.Inception V3 is further
The structure of Inc eption model is optimized, such as 3*3 convolution kernel is split as 1*3 convolution kernel and 3*1 convolution kernel.In the present invention
In embodiment, on the basis of the Inception V3 model obtained by preset visible database pre-training, benefit
Transfer learning is carried out with " the visual range area data collection " under business scenario to obtain the mind of the classification in the embodiment of the present invention
Through network model.
In some embodiments, in the training process of Classification Neural model, according to interference content in abnormal image
In appearance form and vision shape, abnormal image is divided into the type of multiple priori.Interfere content in abnormal image
Appearance form can be interference content and surround to original contents, interfere content in the two sides up and down of original contents or left and right two
Side interferes the vision shape of content to can be black region, frosted glass region or ad content unrelated with original contents etc.
Deng.Classification Neural model is trained by the type of above multiple priori, type here characterizes appearance form simultaneously
And vision shape.The potential different of normal picture and each type is belonging respectively to by Classification Neural model come forecast image
The probability of normal image, using the prediction result of maximum probability as classification results.Here Classification Neural model can be volume
Product neural network (CNN, Convolutional Neural Network), the output of convolutional layer is the spy extracted from image
Sign, then the feature extracted by the connection of full articulamentum, and then pass through maximum likelihood (softmax) function layer for extracted spy
Sign, which is converted to, to be respectively corresponded the normal picture for not being implanted with interference content and is implanted with the general of the potential abnormal image for interfering content
Rate, potential abnormal image is implanted with interference content with any one presentation mode in a variety of presentation modes of priori here.
For example, the type of interference content can be distributed in the two sides up and down of original contents for black region, the type is to make
For one kind in numerous potential abnormal images of priori data training Classification Neural model, here, Classification Neural mould
The output of type is the probability that image belongs to normal picture and image with interference content, and interfering the type of content is black region
Domain is distributed in the probability of the two sides up and down of original contents.Image is input to after Classification Neural, and output corresponds to image
The image that corresponds to that the probability for belonging to normal picture is less than output has interference content, and interfering the type of content is black region
It is distributed in the probability of the two sides up and down of original contents, it is hereby achieved that the type of image is that image has interference content, and does
Disturb the two sides up and down that the type of content is distributed in original contents for black region.
In a step 102, the interference content when being implanted with interference content in classification results characterization image, in detection image
Boundary position between original contents.
In some embodiments, when being implanted with interference content in the characterization of the classification results obtained in the step 101 image, inspection
Interference content in altimetric image and the boundary position between original contents.Interference content in image can be black region, hair
Glassy zone or other contents unrelated with original contents, the original contents in image are that have the portion of actual content meaning
Point.
Referring to Fig. 3 C, based on Fig. 3 A, in step 102 when being implanted with interference content in classification results characterization image, detection
Interference content in image and the boundary position between original contents can be realized by step 1021-1 024.
In step 1021, image is pre-processed to remove the noise in image.
In some embodiments, the noise on image will affect the precision of edge detection, and noise is easily recognizable as pseudo-side
Edge, therefore image pixel matrix and Gaussian filter are subjected to convolution operation to remove noise, this process be referred to as be
Image is carried out smoothly, size is the Gaussian filter core H of (2k+1) x (2k+1)ijGrowth equation formula be given by:
Here i and j respectively indicates row serial number and column serial number in Gaussian convolution nuclear matrix, and k is positive integer, and σ is constant,
Gaussian convolution core size will affect the performance of edge detection, and size is bigger, get in edge detection process to the susceptibility of noise
It is low, but the position error of edge detection will also have increase.
In step 1022, gradient magnitude and the direction of each pixel in image are determined, to obtain edge pixel
The Candidate Set of point.
In some embodiments, by calculating the Candidate Set of the available edge pixel point of image gradient, the gradient of image
The difference being equivalent between 2 adjacent pixels, gradient here more more obvious than other parts in the grey scale change of contour edge
It is equivalent to the change rate of gray value.It is calculated by Robert (Roberts), Pu Luyite (Prewitt), Suo Boer (Sobel) etc.
Son can calculate gradient value horizontally and vertically, illustrated how by taking Sobel as an example below calculate gradient intensity and
Direction.
In rectangular coordinate system, the direction of Sobel operator is as shown in Figure 4.Fig. 4 is Sobel provided in an embodiment of the present invention
The direction schematic diagram of operator.The Sobel operator in the direction x and y is respectively as follows:
Wherein SxThe Sobel operator for indicating the direction x, for detecting the boundary in the direction y, Sy indicates that the Sobel in the direction y is calculated
Son, for detecting the boundary in the direction x, boundary direction and gradient direction are vertical.
If the window of a 3x3 is A in image, the pixel that calculate gradient is e, then carries out convolution with Sobel operator
Later, gradient value of the pixel e in the direction x and y is respectively as follows:
Wherein * is convolution symbol, and all elements are added summation in sum representing matrix, and the operator of border detection returns horizontal
Thus the first derivative values of Gx and the vertical direction Gy can determine the gradient G and direction θ of pixel, specific formula is as follows:
In step 1023, the Candidate Set of edge pixel is carried out simplifying processing, based on the edge pixel point after simplifying
Candidate Set formed image profile diagram.
In some embodiments, after carrying out gradient calculating to image, it is based only on the edge still very mould of gradient value extraction
Paste, provided in an embodiment of the present invention to simplify treatment process then be that all gradient values except local maximum are suppressed to 0, specifically
It is as follows to simplify process: comparing the gradient intensity of current pixel and the neighborhood pixels on positive and negative gradient direction, if current pixel
The gradient intensity of point is greater than the gradient intensity of the neighborhood pixels on positive and negative gradient direction, then current pixel point is left edge
Point, otherwise current pixel point is suppressed, i.e., the gray value of current pixel point is set as 0, to simplify the Candidate Set of pixel, made
The profile diagram recognized is more simplified clearly.
In step 1024, it is based on profile diagram, obtains the boundary position between the interference content and original contents in image.
Based on the profile diagram obtained in step 1023, the side interfered between content and original contents in image can be obtained
Boundary position.After having carried out simplifying processing, remaining pixel can more accurately indicate the actual edge in image, however,
Some edge pixels due to caused by noise and color change are still had in image, in order to overcome these noises and color to become
Response, needs to be filtered edge pixel using Grads threshold caused by changing.
As an example, Grads threshold here includes high threshold and Low threshold (being less than high threshold), when the ladder of edge pixel
When angle value is higher than high threshold, then strong edge pixel is marked as；When the gradient value of edge pixel is less than high threshold and is greater than
When Low threshold, then weak edge pixel is marked as；When the gradient value of edge pixel is less than Low threshold, then the edge pixel can quilt
Inhibit, the high threshold of recommendation and the ratio of Low threshold are between 2 and 3.Thus, it, can be in order to keep the edge obtained more accurate
Step 1024 is implemented by following steps.
The first step retains gradient intensity in profile diagram and is greater than the pixel of first gradient threshold value, and inhibits gradient intensity small
In the pixel of the second Grads threshold, wherein first gradient threshold value is greater than the second Grads threshold.Here first gradient threshold value is
Above-mentioned high threshold, the second Grads threshold here are above-mentioned Low threshold.Inhibition to pixel is by by pixel
Gray value is set as 0 realization.
Pixel of the gradient intensity between first gradient threshold value and the second Grads threshold is labeled as weak side by second step
Edge pixel.Here pixel of the gradient intensity between first gradient threshold value and the second Grads threshold is as above-mentioned in profile diagram
Gradient value is less than high threshold and is greater than the pixel of Low threshold.
Third step, when the pixel for thering is gradient intensity to be greater than the first gradient threshold value in the weak edge pixel neighborhood of a point
When point, retain the weak edge pixel point, when being greater than described first without gradient intensity in the weak edge pixel neighborhood of a point
When the pixel of Grads threshold, inhibit the weak edge pixel point.
Up to the present, the pixel that gradient intensity is greater than first gradient threshold value in retained profile diagram has been determined
Then there is uncertainty for being marked as the pixel of weak edge pixel for actual edge.These pixels can be reality
What edge extracting came out, it is also possible to be identified because of noise or color change.In general, weak as caused by actual edge
The pixel that edge pixel will be connected to gradient intensity in retained profile diagram and be greater than first gradient threshold value, in order to track boundary
Connection, can search weak edge pixel and its 8 neighborhood territory pixels, as long as one of them is that gradient is strong in retained profile diagram
Degree is greater than the pixel of first gradient threshold value, then the weak marginal point can be left true edge pixel point.
4th step, based on the pixel retained in profile diagram, combination formed interference content in image and original contents it
Between boundary position.
For the pixel retained in the profile diagram obtained in the third step, when anomalous content is to be presented on regular domain
Interior, then anomalous content and original contents are straight line in the boundary line of boundary position, and usual profile diagram is binary map, profile by
The connection of white pixel point, the accounting of white pixel point in every row or each column is judged by being traversed to profile diagram, if
The white pixel of the row or column point accounting is more than white pixel point proportion threshold value, it is determined that the row or the column are above-mentioned sides
The boundary of boundary's straight line, as original contents and interference content, based on the above-mentioned technical idea traversed to profile diagram, the 4th step
In based on the pixel retained in profile diagram, combination forms the interference content in image and the tool of the boundary position between original contents
Body is realized in the following way.
It determines white pixel point proportion threshold value, traverses profile diagram, obtain the ratio of the white pixel point of every a line in profile diagram
Example will be greater than the position of the row of white pixel point proportion threshold value, the interference content being determined as in image and original in the profile diagram
Boundary position between beginning content.
In some embodiments, anomalous content is distributed in the two sides up and down of original contents, and the distributed areas of anomalous content are
Rectangle obtains the ratio of the white pixel point of every a line in profile diagram, will be greater than white pixel point ratio then by traversal profile diagram
The position of the row of example threshold value, is determined as the interference content in image and the boundary position between original contents.
In some embodiments, anomalous content is distributed in the left and right sides of original contents, and the distributed areas of anomalous content are
Rectangle obtains the ratio of the white pixel point of each column in profile diagram, will be greater than white pixel point ratio then by traversal profile diagram
The position of the column of example threshold value, is determined as the interference content in image and the boundary position between original contents.
In some embodiments, anomalous content can be looped around the surrounding of original contents, and the distributed areas of anomalous content are
When combining the circle zone formed by rectangle, then by traversal profile diagram, the white pixel point of each column in profile diagram is obtained
The ratio of the white pixel point of every a line in ratio and profile diagram, will be greater than the column of white pixel point proportion threshold value position and
Greater than the position of the row of white pixel point proportion threshold value, it is determined as the interference content in image and the boundary bit between original contents
It sets.
In some embodiments, according to the sharpness of border degree of interference content, white pixel point proportion threshold value, white picture are determined
Vegetarian refreshments proportion threshold value and the sharpness of border degree of interference content are positively correlated.If threshold value sets excessively high, cause not identify phase
The boundary answered, so that the picture number recalled is inadequate；If threshold value sets too low, it will lead to and not known for the pixel on boundary
Not at boundary, the image for causing the later period to be recalled actually is not belonging to influence the abnormal image of viewing experience, thus, white here
Pixel proportion threshold value is determined according to sharpness of border degree.
In some embodiments, interference content can be distributed in the two sides of original contents for black region, referring to Fig. 5 A, figure
5A is the image that interference content provided in an embodiment of the present invention is black region, when interfering content is monochrome image, for example, single
Color region can be black region 501A, and the presentation region of original contents is visual range region 502A, determines white pixel point
Proportion threshold value is the first proportion threshold value.First proportion threshold value is that the special interference content that corresponds to is white in the case where monochrome image
Colour vegetarian refreshments proportion threshold value.First proportion threshold value is obtained according to experimental data, first sets the first proportion threshold value as a value,
The first proportion threshold value based on setting is handled image to calculate the visual range region that original contents are presented, and acquisition makes
The accuracy rate evaluated and tested with the embodiment of the present invention.Assess whether properly the first proportion threshold value is set to by accuracy rate.
In some embodiments, interference content can be frosted glass area distribution in the two sides of original contents, referring to Fig. 5 B,
Fig. 5 B is the image that interference content provided in an embodiment of the present invention is frosted glass region, for example, frosted glass region can be Fig. 5 B
In frosted glass region 501B, the presentation region of original contents is visual range region 502B, when interference content be frosted glass figure
When picture, determine that white pixel point proportion threshold value is the second proportion threshold value.Second proportion threshold value is that the special interference content that corresponds to is
White pixel point proportion threshold value in the case where frosted glass image.Second proportion threshold value is also to be obtained according to experimental data,
Setting method and the first proportion threshold value are similar.
In some embodiments, interference content can be its other than the monochrome image and the frosted glass image
His image, is distributed in the two sides of original contents, for example, interference content can be ad content, it is the present invention referring to Fig. 5 C, Fig. 5 C
The interference content that embodiment provides is that the image of other images other than the monochrome image and the frosted glass image shows
It is intended to, for example, other images other than monochrome image and frosted glass image can be the advertising image in Fig. 5 C, advertisement figure
Advertising image region 501C as being presented on the two sides up and down of original contents, the presentation region of original contents is visual range region
502C determines white picture when interfering content is other images other than the monochrome image and the frosted glass image
Vegetarian refreshments proportion threshold value is third proportion threshold value.Third proportion threshold value is specially to correspond to the case where interference content is frosted glass image
Under white pixel point proportion threshold value.Third proportion threshold value is also to be obtained according to experimental data, setting method and the first ratio
Example threshold value is similar.
Since the boundary line of monochromatic areas and original contents is more clear than the boundary line of frosted glass and original contents, so first
Proportion threshold value be greater than the second proportion threshold value, due to other than the monochrome image and the frosted glass image other images and
The readability of the boundary line of original contents is between monochromatic areas and frosted glass, and therefore, third proportion threshold value is between the first ratio
Example threshold value is greater than the second proportion threshold value.
In some embodiments, the Relative distribution mode for interfering content and original contents and the vision for interfering content are presented
Form has multiple types, for example, the vision appearance form of interference content can be monochromatic areas, can be frosted glass special efficacy,
It is also possible to advertising information etc., after being classified by Classification Neural model to image, for certain kinds
The image of the interference content of type can be based on corresponding in certain types of interference when carrying out boundary alignment for its profile diagram
The white pixel proportion threshold value of appearance carries out boundary alignment, so that boundary alignment is more accurate, white pixel proportion threshold value
Accuracy rate assessment also with method is constantly adjusted.
In step 103, according to the distribution side of the interference content of boundary position and the classification results characterization detected
Formula determines the visual range region that original contents are presented in image.According to boundary position and the classification results characterization detected
Interference content distribution mode, can determine in image present original contents visual range region.
At step 104, the area accounting in visual range region is determined.Based on being in the image obtained in step 103
The visual range region of existing original contents, can determine the area accounting in visual range region.
In step 105, when the area accounting in visual range region is less than area accounting threshold value, determine that image belongs to shadow
Ring the abnormal image of viewing experience.When the area accounting in visual range region is less than area accounting threshold value, determine that image belongs to
Influence the abnormal image of viewing experience.
Referring to Fig. 3 D, step 106-108 can also be performed after executing the step 105 based on Fig. 3 A.
In step 106, it when abnormal image includes multiple same or similar subgraphs, is carried out in each subgraph
Slide window processing, and detect the standard deviation of the grayscale image pixel value of same position window in each subgraph.
In step 107, when the difference between standard deviation is less than standard deviation threshold method, the image of same position window is determined
For similar image, and same position window is determined as similar window.
In step 108, when the accounting of the quantity of similar window is greater than similar window accounting threshold value, by abnormal image
Classification results are updated to normal picture.
It is the suitable of sliding average variance detection technique provided in an embodiment of the present invention referring to Fig. 6 A and Fig. 6 B, Fig. 6 A and Fig. 6 B
Answer application scenarios schematic diagram.Characteristics of image under this application scenarios such as is schemed comprising multiple same or similar subgraphs
Pattern in three regions of upper, middle and lower of picture is roughly the same, but again not quite identical, and there are downward shifts, intermediate region band word
Phenomena such as curtain plus filter.For example, the image subject presented in upper area 601A, intermediate region 602A and lower area 603A
Be it is similar, difference is only that in the 602A of intermediate region there are subtitle, does not have subtitle black surround, upper area in lower area 603A
The image subject presented in 601B, intermediate region 602B and lower area 603B be also it is similar, difference be only that image subject
There is certain deviation, this kind of image may be identified as abnormal image in step 101-105, but in practical applications, this kind of
Image simultaneously is not belonging to influence the abnormal image of viewing experience, and therefore, it is necessary to carry out type update to such image.In step 106-
The matrix window that one size is N*M is set in 108, is calculated separately in the region of multiple similar subgraph pictures in multiple similar sons
The standard deviation of the grayscale image pixel value of same position window in the region of image.When three windows being calculated standard deviation it
Between difference be less than standard deviation threshold method when, then judge the image of this position be it is similar, otherwise judge the image of this position
Be it is dissimilar, finally calculate the accounting of similar number of windows, if accounting be more than similar window accounting threshold value, judge multiple
Multiple similar subgraphs in the region of similar subgraph picture are updated to normogram as identical, by the classification results of this abnormal image
Picture.
Referring to Fig. 3 E, be based on Fig. 3 A, step 109 can also be performed before executing step 101, execution step 105 it
Afterwards, step 110 can also be performed.
In step 109, decoding obtains multiple image from video.
In step 110, when the number for belonging to abnormal image in the multiple image that decoding obtains is greater than outlier threshold, really
Determine video and belongs to the anomalous video for influencing viewing experience.
Performed image processing method, which can be, in step 101-105 is directed to what an image was carried out.For
For the video that user uploads, video cover can be extracted by step 109 and video is carried out to take out frame processing, thus in step
The detection of abnormal image is executed in rapid 101-105 to every frame image.When in the multiple image that decoding determining in step 10 obtains
When belonging to the number of abnormal image greater than outlier threshold, determine that video belongs to the anomalous video for influencing viewing experience.Here different
Normal threshold value is the data constantly tested and the performance to method is assessed, the parameter that can be assessed method progressive
For accuracy rate and recall rate and the comprehensive performance evaluation parameter F obtained based on accuracy rate and recall rate.
As an example, the calculating process of recall rate is as follows, manual examination and verification and through the invention are carried out respectively to great amount of images
Embodiment calculates visual range region area accounting to judge abnormal image, and the actual number of image can be 1000 here
, it has identified 100 abnormal images, really be the number of abnormal image in this 100 abnormal images has been 90, is i.e. identification standard
True rate is 90%, but has 110 abnormal images in actually 1000 images, then recall rate is 82%.By setting not
Same outlier threshold obtains different comprehensive performance evaluation parameter, accuracy rate and recall rates, obtains further according to assessment different
Comprehensive performance evaluation parameter, accuracy rate and the anti-value for releasing threshold value of recall rate, the mode of above-mentioned given threshold are equally applicable to
Other threshold setting procedures in the embodiment of the present invention.
The calculation formula of comprehensive performance evaluation parameter is as follows:
The application of image processing method provided in an embodiment of the present invention can also be photo, in social software, need
Content auditing is carried out to photo, when photo is judged as abnormal image in content auditing, photo can be intercepted.
In some embodiments, the multiple pictures in photograph album that can be uploaded to user carry out visual range region meter respectively
It calculates, thus judge whether photo is abnormal image, it, can be right when the number of photos for being judged as abnormal image is more than outlier threshold
Entire photograph album is intercepted, or the displaying permission of limitation photograph album.
In some embodiments, visual range region provided in an embodiment of the present invention can be performed a plurality of times to a certain photo
Calculation method is averaged to the multiple each area accounting result obtained that calculates, is based on average area accounting, judges that photo is
It is no to belong to abnormal image, to improve the accuracy rate for judging abnormal image.
In the following, will illustrate exemplary application of the embodiment of the present invention in an actual application scenarios.
The embodiment of the present invention proposes a kind of image processing method, and this method mainly includes three parts: picture classification part,
Border detection part, sliding average variance detection part.
Picture classification part carries out image classification using convolutional neural networks, and classification includes normal picture, two sides black region
Domain, two sides frosted glass, two sides contain static main body etc..
Picture contour feature is extracted using Kenny's (Canny) edge detecting technology in border detection part, and according to convolution mind
Image classification result through network sets different white pixel proportion threshold values, to determine boundary position.
Sliding average variance detection technique calculates the similitude of lower regions and intermediate region, prevents normal picture from accidentally being known
Not.
It is directed to the image resource of video type, the exemplary application process of the embodiment of the present invention is illustrated in fig. 7 shown below, Fig. 7
For the Method And Principle flow chart of image procossing provided in an embodiment of the present invention.It is firstly received the visual range identification for video
Service request obtains the video surface plot and video content of video, carries out uniform sampling to video content and takes out frame to combine shape
At image collection, classified using convolutional neural networks to the image in image collection, classification includes that normal picture, two sides are black
Border region, diamond wool glassy zone, two sides contain static main body, stop subsequent detection step if image recognition is normal picture
Suddenly, the visual range boundary of image is calculated using Canny edge detecting technology, for identification the white pixel proportion threshold value on boundary
It is set according to the generic of previous step image, the image accidentally recalled is exempted using sliding average variance detection technique, to mention
The accuracy rate of high abnormal image judgement, handles each of image collection image as described above, works as exception
When the number of image has been more than outlier threshold, determine that video is that anomalous video in practical applications can be right in recommender system
Anomalous video is suppressed or is intercepted.
In the following, the process step that will illustrate Canny edge detecting technology provided in an embodiment of the present invention.
Canny edge detecting technology is the boundary of the profile by identifying image, judgement and positioning image visual range
Line.Noise filtering is crossed by Gaussian filter first, smoothed image, the noise on image will affect the precision of edge detection, be easy
It is identified as pseudo-edge, therefore image pixel matrix and Gaussian filter is subjected to convolution operation, removes noise；Calculate image ladder
Size and Orientation is spent, by calculating the Candidate Set of the available contour edge of image gradient, level can be calculated by operator
The first derivative values in direction and vertical direction can determine the gradient direction and size of pixel；It carries out at non-maxima suppression
Reason, compares the gradient intensity of current pixel and the neighborhood pixels on positive and negative gradient direction, so that contour edge Candidate Set is simplified,
Simplify the contours profiles recognized more clearly；Dual threshold detection is carried out, non-edge profile point is further filtered, strong ladder is set
Spend threshold value and weak Grads threshold.
After obtaining image outline figure using Canny edge detection, black region, frosted glass, the body region containing static state and figure
The junction of the practical visual range of picture can be identified as profile, and the profile is straight line.Image outline figure is binary map,
Profile is connected by white pixel point.Therefore accounting for for white pixel point in every row or each column can be judged by traversing profile diagram
Than if the white pixel point accounting of the row is more than threshold value, then it is assumed that be straight line, that is, navigate to visual range boundary.If white
Colour vegetarian refreshments proportion threshold value sets excessively high, then the abnormal image number recalled is inadequate, and there are the feelings that abnormal image is not called back
Shape；If white pixel point proportion threshold value sets too low, it will cause misrecognitions, so the selection of white pixel point proportion threshold value
It is most important.Black region and the boundary line of image subject part are than more visible, and therefore, white pixel point proportion threshold value is arranged
It is higher, because boundary becomes apparent from；Frosted glass region and image subject boundary line generally compared with obscure, therefore, white pixel point ratio
Example threshold value is arranged lower, because boundary is unobvious；The case where being static body region for interference content, white pixel point ratio
Example threshold value is between these；The setting of white pixel point proportion threshold value is determined by experimental result.In general, it needs
Different white pixel point proportion threshold values is set according to image classification result, positioned eventually by white pixel point proportion threshold value
To boundary line.
In the following, the process step that will illustrate sliding average variance detection technique provided in an embodiment of the present invention.
The detection of sliding average variance is to solve accidentally to recall phenomenon to improve accuracy rate, image classification and the edge Canny
Image recognition shown in Fig. 6 A and Fig. 6 B can be that such image is that two side areas contains static main body by detection, but actually this kind of
Image belongs to the specific normal picture of tool.This kind of characteristics of image is obvious, the pattern substantially phase in three regions of upper, middle and lower
Together, but it is again not quite identical, there are downward shift, intermediate region band subtitle, add phenomena such as filter.The detection of sliding average variance
The matrix window that a size is N*M can be arranged in module, and the gray level image of same position window is calculated separately in three regions
The standard deviation of element value.If difference is less than standard deviation threshold method between the standard deviation for three windows being calculated, it is determined that this
The image of position be it is similar, otherwise judge that the image of this position is dissimilar, sliding window is repeatedly to repeat aforesaid operations, most
The accounting of similar number of windows is calculated afterwards, if accounting is more than similar window accounting threshold value, judges upper, middle and lower last zone figure
Piece is identical, and nonrecognition is abnormal image of the two side areas containing static main body.
Referring to Fig. 8, Fig. 8 is image processing method provided in an embodiment of the present invention for content auditing and the stream being recommended to use
Cheng Tu.After getting surface plot and video content, to surface plot and the image set obtained after video content takes out frame processing
Each image in conjunction carries out image classification, if the area accounting of the practical visual range of image is less than area accounting threshold value,
The practical visual range for characterizing image is too small, when the too small picture number of practical visual range has been more than outlier threshold, then right
Video is suppressed, and when classification results characterize in image, there are the regions of above-mentioned presentation interference content, but the reality of image can
Area accounting threshold value is still greater than in area accounting depending on range, then video is marked, and video is used to recommendation side.
In some embodiments, image visual range areas is identified can also be by target detection end to end
Technology carries out, using the visual range region of image as target progress target detection, by target detection frame as a result, can be with
It calculates the area accounting in visual range region and obtains the type of image.
Continue with explanation image processing apparatus 255 provided in an embodiment of the present invention is embodied as the exemplary of software module
Structure, in some embodiments, as shown in Fig. 2, the software module being stored in the image processing apparatus 255 of memory 250 can
To include: image classification module 2551, for extracting the characteristic information of image, described image is carried out according to the characteristic information
Classification；Boundary detection module 2552, for detecting the figure when being implanted with interference content in classification results characterization described image
The boundary position between interference content and original contents as in；Visual range region confirmation module 2553, for according to
The distribution mode of the interference content of the boundary position detected and classification results characterization, determines in described image
The visual range region of the original contents is presented；Area accounting determining module 2554, for determining the visual range region
Area accounting；Image judgment module 2555 is less than area accounting threshold value for the area accounting when the visual range region
When, determine that described image belongs to the abnormal image for influencing viewing experience.
In some embodiments, described image categorization module 2551 is also used to: by Classification Neural model from described
Feature is extracted in image, is the probability for respectively corresponding following classification results by extracted Feature Conversion: not being implanted in interference
The normal picture of appearance, the potential abnormal image for being implanted with interference content；Wherein, the potential abnormal image is in a variety of of priori
Any one presentation mode is implanted with interference content in existing mode.
In some embodiments, the boundary detection module 2552 is also used to: being pre-processed described image to remove
Noise in described image；Gradient magnitude and the direction of each pixel in described image are determined, to obtain edge pixel
The Candidate Set of point；The Candidate Set of the edge pixel point is carried out simplifying processing, the candidate based on the edge pixel point after simplifying
Collection forms the profile diagram of described image；Based on the profile diagram, obtain between the interference content and original contents in described image
Boundary position.
In some embodiments, the boundary detection module 2552 is also used to: it is big to retain gradient intensity in the profile diagram
In the pixel of first gradient threshold value, and inhibit gradient intensity less than the pixel of the second Grads threshold, wherein first ladder
It spends threshold value and is greater than second Grads threshold；By gradient intensity between the first gradient threshold value and second Grads threshold
Pixel, be labeled as weak edge pixel point；When have in the weak edge pixel neighborhood of a point gradient intensity be greater than described first
When the pixel of Grads threshold, retain the weak edge pixel point, when strong without gradient in the weak edge pixel neighborhood of a point
When degree is greater than the pixel of the first gradient threshold value, inhibit the weak edge pixel point；Based on what is retained in the profile diagram
Pixel combines the boundary position between the interference content to be formed in described image and original contents.
In some embodiments, the boundary detection module 2552 is also used to: determining white pixel point proportion threshold value；Traversal
The profile diagram obtains the ratio of the white pixel point of every a line in the profile diagram；It is described white by being greater than in the profile diagram
The position of the row of colour vegetarian refreshments proportion threshold value, is determined as the interference content in described image and the boundary bit between original contents
It sets.
In some embodiments, the boundary detection module 2552 is also used to: according to the sharpness of border of the interference content
It spends and determines white pixel point proportion threshold value, wherein the sharpness of border of the white pixel point proportion threshold value and the interference content
Degree is positively correlated.
In some embodiments, the boundary detection module 2552 is also used to: when the interference content is monochrome image,
Determine that the white pixel point proportion threshold value is the first proportion threshold value；When the interference content is frosted glass image, institute is determined
Stating white pixel point proportion threshold value is the second proportion threshold value；When the interference content is in addition to the monochrome image and the hair glass
When other images except glass image, determine that the white pixel point proportion threshold value is third proportion threshold value；Wherein, the third
Proportion threshold value is greater than second proportion threshold value, and the third proportion threshold value is less than first proportion threshold value.
In some embodiments, described device further include:
Sliding average variance detection module 2556, is used for: when the abnormal image includes multiple same or similar subgraphs
When picture, slide window processing is carried out in each subgraph, and detects the grayscale image pixel value of same position window in each subgraph
Standard deviation；When the difference between the standard deviation is less than standard deviation threshold method, determine that the image of the same position window is
Similar image, and the same position window is determined as similar window；When the accounting of the quantity of the similar window is greater than phase
When like window accounting threshold value, the classification results of the abnormal image are updated to normal picture.
In some embodiments, described device further include: Video decoding module 2557 and video judgment module 2558.Depending on
Frequency decoder module 2557 obtains multiframe described image for decoding from video；The video judgment module 2558, for when solution
When belonging to the number of the abnormal image in the multiframe described image that code obtains greater than outlier threshold, determine that the video belongs to shadow
Ring the anomalous video of viewing experience.
The embodiment of the present invention provides a kind of storage medium for being stored with executable instruction, wherein it is stored with executable instruction,
When executable instruction is executed by processor, processor will be caused to execute image processing method provided in an embodiment of the present invention, example
Such as, the method as shown in Fig. 3 A-3E.
In some embodiments, storage medium can be FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface
The memories such as memory, CD or CD-ROM；Be also possible to include one of above-mentioned memory or any combination various equipment.
In some embodiments, executable instruction can use program, software, software module, the form of script or code,
By any form of programming language (including compiling or interpretative code, or declaratively or process programming language) write, and its
It can be disposed by arbitrary form, including be deployed as independent program or be deployed as module, component, subroutine or be suitble to
Calculate other units used in environment.
As an example, executable instruction can with but not necessarily correspond to the file in file system, can be stored in
A part of the file of other programs or data is saved, for example, being stored in hypertext markup language (HTML, Hyper Text
Markup Language) in one or more scripts in document, it is stored in the single file for being exclusively used in discussed program
In, alternatively, being stored in multiple coordinated files (for example, the file for storing one or more modules, subprogram or code section).
As an example, executable instruction can be deployed as executing in a calculating equipment, or it is being located at one place
Multiple calculating equipment on execute, or, be distributed in multiple places and by multiple calculating equipment of interconnection of telecommunication network
Upper execution.
In conclusion through the embodiment of the present invention, depth network and biography are combined during picture visual range calculates
Characteristics of image of uniting reuses edge detecting technology and calculates picture profile, root using Classification Neural model identification picture classification
According to the different white pixel point proportion threshold values of different classes of setting, picture profile diagram and white pixel point proportion threshold value positioning figure are utilized
The practical visual range boundary of piece finally exempts specific pictures using sliding average variance detection technique, improves accuracy rate.This hair
The method that bright embodiment provides can identify the visual range region of image on the basis of image classification, flat finally by sliding
Mean square error technique exempts particular image, avoids image and accidentally recalls, improves abnormal image recognition accuracy, to save big
Amount audit manpower suppresses a large amount of low-quality photos and videoies convenient for intercepting, improves the viewing experience of user.
The above, only the embodiment of the present invention, are not intended to limit the scope of the present invention.It is all in this hair
Made any modifications, equivalent replacements, and improvements etc. within bright spirit and scope, be all contained in protection scope of the present invention it
It is interior.
Claims (10)
1. a kind of image processing method, which is characterized in that the described method includes:
The characteristic information for extracting image, classifies to described image according to the characteristic information；
When classification results characterization described image in be implanted with interference content when, detect described image in interference content with it is original interior
Boundary position between appearance；
According to the distribution mode for the interference content that the boundary position detected and the classification results characterize, really
Determine the visual range region that the original contents are presented in described image；
Determine the area accounting in the visual range region；
When the area accounting in the visual range region is less than area accounting threshold value, determine that described image belongs to influence viewing body
The abnormal image tested.
2. the method according to claim 1, wherein it is described extract image characteristic information, according to the feature
Information classifies to described image, comprising:
Feature is extracted from described image by Classification Neural model, extracted Feature Conversion is respectively corresponded following
The probability of classification results:
The potential abnormal image for not being implanted with the normal picture of interference content, being implanted with interference content；
Wherein, the potential abnormal image is implanted in interference with any one presentation mode in a variety of presentation modes of priori
Hold.
3. the method according to claim 1, wherein it is described detection described image in interference content with it is original interior
Boundary position between appearance, comprising:
Described image is pre-processed to remove the noise in described image；
Gradient magnitude and the direction of each pixel in described image are determined, to obtain the Candidate Set of edge pixel point；
The Candidate Set of the edge pixel point is carried out simplifying processing, the Candidate Set based on the edge pixel point after simplifying forms institute
State the profile diagram of image；
Based on the profile diagram, the boundary position between the interference content and original contents in described image is obtained.
4. according to the method described in claim 3, it is characterized in that, it is described be based on the profile diagram, obtain described image in
Interfere the boundary position between content and original contents, comprising:
Retain gradient intensity in the profile diagram and be greater than the pixel of first gradient threshold value, and inhibits gradient intensity less than the second ladder
Spend the pixel of threshold value, wherein the first gradient threshold value is greater than second Grads threshold；
By pixel of the gradient intensity between the first gradient threshold value and second Grads threshold, it is labeled as weak edge picture
Vegetarian refreshments；
When the pixel for thering is gradient intensity to be greater than the first gradient threshold value in the weak edge pixel neighborhood of a point, retain institute
Weak edge pixel point is stated, when the picture for being greater than the first gradient threshold value in the weak edge pixel neighborhood of a point without gradient intensity
When vegetarian refreshments, inhibit the weak edge pixel point；
Based on the pixel retained in the profile diagram, combination forms the interference content in described image and between original contents
Boundary position.
5. according to the method described in claim 4, it is characterized in that, described based on the pixel retained in the profile diagram, group
Conjunction forms the interference content in described image and the boundary position between original contents, comprising:
Determine white pixel point proportion threshold value；
The profile diagram is traversed, the ratio of the white pixel point of every a line in the profile diagram is obtained；
The position of the row of the white pixel point proportion threshold value, the interference being determined as in described image will be greater than in the profile diagram
Boundary position between content and original contents.
6. according to the method described in claim 5, it is characterized in that, the determining white pixel point proportion threshold value, comprising:
White pixel point proportion threshold value is determined according to the sharpness of border degree of the interference content, wherein the white pixel point ratio
Example threshold value and the sharpness of border degree of the interference content are positively correlated.
7. according to the method described in claim 5, it is characterized in that, the determining white pixel point proportion threshold value, comprising:
When the interference content is monochrome image, determine that the white pixel point proportion threshold value is the first proportion threshold value；
When the interference content is frosted glass image, determine that the white pixel point proportion threshold value is the second proportion threshold value；
When the interference content is other images other than the monochrome image and the frosted glass image, described in determination
White pixel point proportion threshold value is third proportion threshold value；
Wherein, the third proportion threshold value is greater than second proportion threshold value, and the third proportion threshold value is less than described first
Proportion threshold value.
8. method according to any one of claims 1 to 7, which is characterized in that the method also includes:
When the abnormal image includes multiple same or similar subgraphs, slide window processing is carried out in each subgraph, and
Detect the standard deviation of the grayscale image pixel value of same position window in each subgraph；
When the difference between the standard deviation is less than standard deviation threshold method, determine that the image of the same position window is similar diagram
Picture, and the same position window is determined as similar window；
When the accounting of the quantity of the similar window is greater than similar window accounting threshold value, by the classification results of the abnormal image
It is updated to normal picture.
9. method according to any one of claims 1 to 7, which is characterized in that the method also includes:
Decoding obtains multiframe described image from video；
When the number for belonging to the abnormal image in the multiframe described image that decoding obtains is greater than outlier threshold, the view is determined
Frequency belongs to the anomalous video for influencing viewing experience.
10. a kind of image processing apparatus, which is characterized in that described device includes:
Image classification module classifies to described image according to the characteristic information for extracting the characteristic information of image；
Boundary detection module, for detecting in described image when being implanted with interference content in classification results characterization described image
Interference content and original contents between boundary position；
Visual range region confirmation module, what boundary position and the classification results for detecting according to characterized
The distribution mode of the interference content, determines the visual range region that the original contents are presented in described image；
Area accounting determining module, for determining the area accounting in the visual range region；
Image judgment module, described in determining when the area accounting in the visual range region is less than area accounting threshold value
Image belongs to the abnormal image for influencing viewing experience.
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